Quantitation of cancer treatment response by 2-[18F]FDG PET/CT: multi-center assessment of measurement variability using AUTO-PERCIST™

Joo Hyun O; Su Jin Lim; Hao Wang; Jeffrey P Leal; Hui-Kuo G Shu; Richard L Wahl; QIN PET Readers

doi:10.1186/s13550-021-00754-1

Quantitation of cancer treatment response by 2-[¹⁸F]FDG PET/CT: multi-center assessment of measurement variability using AUTO-PERCIST™

EJNMMI Res. 2021 Feb 12;11(1):15. doi: 10.1186/s13550-021-00754-1.

Authors

Joo Hyun O¹, Su Jin Lim², Hao Wang², Jeffrey P Leal³, Hui-Kuo G Shu⁴, Richard L Wahl^{5

6}; QIN PET Readers

Affiliations

¹ Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
² Division of Biostatistics and Bioinformatics, Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
³ Division of Nuclear Medicine, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁴ Department of Radiation Oncology, The Emory Clinic, Atlanta, GA, USA.
⁵ Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA. rwahl@wustl.edu.
⁶ Washington University School of Medicine, Mallinckrodt Institute of Radiology, 510 South Kingshighway Blvd, Campus Box 8131, St. Louis, MO, 63110, USA. rwahl@wustl.edu.

Abstract

Background: The aim of this study was to assess the reader variability in quantitatively assessing pre- and post-treatment 2-deoxy-2-[¹⁸F]fluoro-D-glucose positron emission tomography/computed tomography ([¹⁸F]FDG PET/CT) scans in a defined set of images of cancer patients using the same semi-automated analytical software (Auto-PERCIST™), which identifies tumor peak standard uptake value corrected for lean body mass (SUL_peak) to determine [¹⁸F]FDG PET quantitative parameters.

Methods: Paired pre- and post-treatment [¹⁸F]FDG PET/CT images from 30 oncologic patients and Auto-PERCIST™ semi-automated software were distributed to 13 readers across US and international sites. One reader was aware of the relevant medical history of the patients (read_reference), whereas the 12 other readers were blinded to history but had access to the correlative images. Auto-PERCIST™ was set up to first automatically identify the liver and compute the threshold for tumor measurability (1.5 × liver mean) + (2 × liver standard deviation [SD]) and then detect all sites with SUL_peak greater than the threshold. Next, the readers selected sites they believed to represent tumor lesions. The main performance metric assessed was the percent change in the SUL_peak (%ΔSUL_peak) of the hottest tumor identified on the baseline and follow-up images.

Results: The intra-class correlation coefficient (ICC) for the %ΔSUL_peak of the hottest tumor was 0.87 (95%CI: [0.78, 0.92]) when all reads were included (n = 297). Including only the measurements that selected the same target tumor as the read_reference (n = 224), the ICC for %ΔSUL_peak was 1.00 (95%CI: [1.00, 1.00]). The Krippendorff alpha coefficient for response (complete or partial metabolic response, versus stable or progressive metabolic disease on PET Response Criteria in Solid Tumors 1.0) was 0.91 for all reads (n = 380) and 1.00 including for reads with the same target tumor selection (n = 270).

Conclusion: Quantitative tumor [¹⁸F]FDG SUL_peak changes measured across multiple global sites and readers utilizing Auto-PERCIST™ show very high correlation. Harmonization of methods to single software, Auto-PERCIST™, resulted in virtually identical extraction of quantitative tumor response data from [¹⁸F]FDG PET images when the readers select the same target tumor.

Keywords: Quantification; Response assessment; [18F]FDG PET/CT.

Abstract

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